• Corpus ID: 239016088

Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees

@article{Qian2021ResultDB,
  title={Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees},
  author={Chao Qian and Danqin Liu and Zhi-Hua Zhou},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.09332}
}
Given a ground set of items, the result diversification problem aims to select a subset with high “quality” and “diversity” while satisfying some constraints. It arises in various real-world artificial intelligence applications, such as web-based search, document summarization and feature selection, and also has applications in other areas, e.g., computational geometry, databases, finance and operations research. Previous algorithms are mainly based on greedy or local search. In this paper, we… 

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